Recommendation systems seek to identify content and/or products that are consistent with individual user preferences. Oftentimes, insights from a recommendation system may be applied towards creating a personalized browsing experience, which can increase a website's traffic and/or sales. For example, a recommendation system may suggest material (e.g., news articles, music, videos) that best interests users of an online content provider. Similarly, a web retailer may deploy a recommendation system that targets its users with products that are more likely to be purchased by each user.
Conventional recommendation systems rely primarily on a priori knowledge of individual user preferences in order to generate recommendations. That is, recommendations from conventional recommendation systems are generally guided by a user's history of past interactions (e.g., browsing, searches, and/or purchases) with a website. As such, conventional recommendation systems often perform poorly in the absence of existing data such as when encountering new users to a website. Moreover, even when individual user preferences are known, conventional recommendation systems tend to single-mindedly exploit this existing data while foregoing opportunities to discover more through exploration of individual users' potential preferences. Consequently, the performance of conventional recommendation systems may further stagnate over time.